Machine Learning for Water and Environmental Modeling in the Sacramento–San Joaquin Delta of California: A 30-Year Retrospective and Future Prospects

The Sacramento–San Joaquin Delta (Delta) is a critical hub for California’s water supply and ecological health, necessitating rapid and accurate modeling for decision-support. This paper reviews three decades of machine learning (ML) applications in the Delta, tracing the evolution from early artificial neural networks (ANNs) developed in the 1990s for salinity estimation to modern integration within operational planning tools like CalSim. Recent advancements highlight a shift toward deep learning and hybrid physics-informed models, expanding applications to water quality forecasting, ecosystem monitoring, toxigenic and disaster risk assessment, and infrastructure resilience. Despite these successes, widespread operational deployment remains constrained by critical gaps in model interoperability, generalizability, interpretability, and operationalization. To address these challenges, we propose a strategic roadmap emphasizing standardized ML development protocols, multi-modal data fusion, digital twins, generative artificial intelligence (AI), explainable AI, and AI agents. This roadmap aims to transform isolated research ML models into a cohesive, transparent, and adaptive decision-support framework for sustainable water and environmental management in the Delta.